Introducing Uppzy RAG: AI That Answers Only From Your Content
How Uppzy uses retrieval-augmented generation to deliver grounded, confident answers from your own documents — without hallucinations.
Large language models are impressive, but by default they don't know your product, your policies, or your customers. Ask a generic chatbot about your refund window and you'll get a confident guess — which is worse than no answer at all.
That's why Uppzy is built around retrieval-augmented generation (RAG). Every answer your chatbot gives is grounded in content you uploaded: docs, help-center articles, product pages, PDFs.
How RAG works at Uppzy
When a customer asks a question, we:
- Convert the question into an embedding vector.
- Search your knowledge base for the most semantically relevant passages.
- Pass those passages — along with the question — to the model as context.
- Return the answer plus a confidence score so you know how reliable it is.
If the retrieved passages don't cover the question, Uppzy says so instead of making something up.
Why this matters for customer support
- No hallucinations on product facts, pricing, or policy.
- Instant updates — change a document, the answer changes.
- Auditable — every response can be traced back to the source paragraph.
Try it on your own content
Upload a few documents, drop our widget on your site, and watch your chatbot start answering in your brand's voice within minutes. The pricing page has a free tier — no credit card needed.
If you want the deeper comparison of why we built around retrieval instead of a generic LLM wrapper, read RAG Chatbot vs Traditional Chatbot. And if you are ready to deploy, the AI Chatbot for Your Website page walks through the setup end-to-end.